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Posted 3 weeks ago

Data Engineering Team Lead

DataSnipper
NL, Netherlands 🇳🇱
  • Onsite
  • Full Time
  • Azure
  • Data Engineering
  • dbt
  • Python
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Job description

  • We are looking for a Data Engineering Team Lead to lead the Data Platform team at DataSnipper
  • Every decision DataSnipper makes about its products runs through the data platform: which features land, which customers are getting value, what we bill for, what we fix next, and which AI capabilities add the most value. You will lead a team of two data engineers who own the systems that make that possible. That means how usage events get captured across a growing set of products, how they become trustworthy models in Snowflake, and how Customer Success, Product and GTM get to answers without waiting on us
  • This is a player-coach role in a small, high-ownership team. You are responsible for the people, the roadmap and the technical direction, and you stay hands-on enough to ship alongside the team and make architectural calls credibly. At DataSnipper, Team Lead is our entry point into engineering management: the same people, delivery and product responsibilities as an Engineering Manager, on a smaller team
  • We are a handful of people serving the whole company, so your judgment about what not to build matters as much as what the team ships
  • The Data Platform team works across three areas: reliable, scalable infrastructure that gets the right data to the right place; a self-service platform so every team can be data-informed without a ticket; and the dashboards and exports customers use to see the value they get from DataSnipper
  • Concretely, you would be walking into billions of usage events flowing from our Excel Add-in, web apps and product backends through Azure Event Hubs into Snowflake; a dbt estate built on medallion principles and managed in dbt Cloud; Terraform-managed Snowflake and Azure infrastructure; and a set of product teams shipping AI agents faster than we can instrument them
  • You will also find real, named open problems rather than a tidy platform: event capture mid-consolidation, multiple methods of user attribution, and a data quality layer that is designed but not yet built. We would rather tell you that up front
  • Lead a team of two data engineers through outcomes, not tasks: set clear goals with the team and trust them to figure out how
  • Make the team measurably better: raise the engineering bar, grow each engineer into bigger problems, and address performance issues directly when they arise
  • Run regular 1:1s, give clear and timely feedback, and own each engineer’s goals and development
  • Foster a culture of experimentation, learning, documentation and operational excellence
  • Take part in hiring and help shape the team’s scope as it evolves
  • Partner closely with Product, Engineering, Customer Success and GTM to shape the team’s roadmap, turning vague data requests into scoped, well-defined work
  • Make the trade-offs a small team serving the whole company needs: what to build, what to defer, and when a request shouldn’t become a pipeline
  • Set the technical direction and make the architectural calls that shape the team’s systems
  • Keep Snowflake performance and cost, tech debt, dependencies and compliance obligations (audit logging, vulnerability remediation, Vanta evidence) visible and under control
  • Be the person other engineering teams come to when they need to instrument something new
  • Contribute to the event ingestion architecture end to end: Azure Event Hubs, Snowpipe, Fivetran, and our shared Python/TypeScript event client libraries
  • Build and review dbt transformation pipelines that stay reliable as volume, source count and model complexity grow
  • Define and enforce event contracts and schemas so product teams can instrument new features without silent breakage downstream
  • Evolve the core data models (event, user, license, company) and integrate new sources such as product backends, MongoDB, HubSpot and billing
  • Guide reverse ETL and activation work that pushes modeled data back into HubSpot, MongoDB, Postgres and GTM reporting
  • Experience on a cloud platform at the infrastructure level
  • An empowerment style of leadership: you lead through outcomes and trust, not by assigning tasks
  • Early evidence of growing engineers and raising a team’s bar, and the willingness to address performance issues directly
  • Bias to action, a strong sense of ownership, and the judgment to prioritize when demand exceeds capacity
  • Excellent communication in English and genuine comfort working directly with non-technical stakeholders
  • At least 1 year leading engineers as a team lead or engineering manager, including 1:1s, feedback, goal setting, performance, and development
  • Production experience with a cloud data warehouse and a modern transformation framework
  • 7+ years in data engineering or a closely related backend/platform role, with a track record of owning a data platform area end to end
  • Strong product aptitude: you can challenge the framing of a request, translate stakeholder needs into technical solutions, and make roadmap trade-offs
  • Solid data modeling fundamentals and the ability to defend a modeling decision to both engineers and business stakeholders
  • Experience with event-driven / streaming ingestion and its failure modes: schema drift, duplication, late data, backfills
  • Experience operating data systems in production with a focus on reliability, observability and maintainability
  • Deep SQL and strong Python, including query optimization and performance tuning on a cloud warehouse
  • Experience hiring engineers or helping a team expand its scope
  • Experience with Terraform, Docker and data governance at scale
  • Experience with product analytics tooling and warehouse-native BI
  • Reverse ETL experience and familiarity with CRM data models or customer success platforms
  • Experience building data products for AI or agent consumption: semantic layers, metrics layers, MCP servers, or governed self-service access
  • Experience in a startup or scale-up, ideally as an early member of a data team